Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/7770#discussion_r36019144
--- Diff: core/src/main/scala/org/apache/spark/ui/ToolTips.scala ---
@@ -62,6 +62,13 @@ private[spark] object ToolTips {
"""Time that the executor spent paused for Java garbage collection
while the task was
running."""
+ val PEAK_EXECUTION_MEMORY =
+ """Execution memory refers to the memory used by internal data
structures created during
+ shuffles, aggregations and joins when Tungsten is enabled. The
value of this accumulator
+ should be approximately the sum of the peak sizes across all such
data structures created
+ in this task. For SQL jobs, this only tracks all unsafe operators,
broadcast joins, and
+ external sort."""
--- End diff --
I don't love the name "Execution Memory" -- I think a user will assume this
is covering *all* memory that isn't for cached rdds. I don't even think that
is 100% accurate for sql, but it'll be even less accurate for non-sql use.
Not that I have much better suggestions ... "Peak Aggregator Memory" maybe?
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